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Record W2922342212 · doi:10.1016/s2214-109x(19)30054-3

Making caesarean section safer for African mothers

2019· letter· en· W2922342212 on OpenAlexaff
Anna Dare

Bibliographic record

VenueThe Lancet Global Health · 2019
Typeletter
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsUniversity of TorontoCentre for Global Health ResearchSt. Michael's Hospital
Fundersnot available
KeywordsCaesarean sectionMedicineObstetricsObservational studyProspective cohort studyPregnancyCohort studyUterine ruptureGynecologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

In many African countries, women giving birth still face substantial risks to their own life and that of their baby. In this issue of The Lancet Global Health, David Bishop and colleagues1 report on maternal and neonatal outcomes after caesarean section in 22 African countries. Their findings suggest that this risk remains true even for women who have access to caesarean delivery in a hospital facility, the highest level of intervention afforded under emergency obstetric care. The 3792 women who had a caesarean section in 2016 form a nested cohort within the African Surgical Outcomes Study (ASOS)—a large prospective observational study of surgical volume and outcomes on the continent that employed a 7-day snapshot data capture method.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.024
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0240.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.378
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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